This notebook contains a set of analyses for analyzing yourwhiteshadow’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
yourwhiteshadow | training | published before 2020 | 43 | 76 |
yourwhiteshadow | validation | published 2020 | 5 | 10 |
yourwhiteshadow | test | published after 2020 | 5 | 10 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
yourwhiteshadow | Variable Setup | 23.3% | 1.4% | 16.65 |
yourwhiteshadow | Asmodee | 32.6% | 2.6% | 12.64 |
yourwhiteshadow | Fantasy Flight Games | 14.0% | 1.2% | 11.98 |
yourwhiteshadow | Collectible Components | 11.6% | 1.8% | 6.60 |
yourwhiteshadow | Pegasus Spiele | 14.0% | 2.2% | 6.32 |
yourwhiteshadow | Post Napoleonic | 7.0% | 1.1% | 6.29 |
yourwhiteshadow | Gamewright | 4.7% | 0.8% | 6.04 |
yourwhiteshadow | Economic | 27.9% | 6.9% | 4.03 |
yourwhiteshadow | Medieval | 18.6% | 4.6% | 4.02 |
yourwhiteshadow | Has Miniatures | 4.7% | 2.0% | 2.28 |
yourwhiteshadow | Crowdfunding Kickstarter | 23.3% | 12.6% | 1.85 |
yourwhiteshadow | Card Game | 51.2% | 29.5% | 1.74 |
yourwhiteshadow | Modular Board | 4.7% | 7.6% | 0.61 |
yourwhiteshadow | Dice Rolling | 14.0% | 28.4% | 0.49 |
yourwhiteshadow | Worker Placement | 0.0% | 3.4% | 0.00 |
yourwhiteshadow | Variable Phase Order | 0.0% | 1.4% | 0.00 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2019 | 286096 | Tapestry | 0.861 | no |
2 | 2012 | 124742 | Android: Netrunner | 0.821 | yes |
3 | 2011 | 96848 | Mage Knight Board Game | 0.685 | no |
4 | 2016 | 205637 | Arkham Horror: The Card Game | 0.650 | yes |
5 | 2018 | 205896 | Rising Sun | 0.613 | no |
6 | 2016 | 169786 | Scythe | 0.574 | yes |
7 | 2019 | 285774 | Marvel Champions: The Card Game | 0.543 | no |
8 | 2013 | 134453 | The Little Prince: Make Me a Planet | 0.464 | no |
9 | 2019 | 281946 | Aftermath | 0.440 | no |
10 | 2016 | 205398 | Citadels | 0.429 | no |
11 | 2019 | 266507 | Clank!: Legacy – Acquisitions Incorporated | 0.426 | no |
12 | 2008 | 37111 | Battlestar Galactica: The Board Game | 0.412 | no |
13 | 2008 | 33107 | Senji | 0.396 | no |
14 | 2019 | 285984 | Last Bastion | 0.386 | no |
15 | 2018 | 256916 | Concordia Venus | 0.384 | no |
16 | 2016 | 167791 | Terraforming Mars | 0.383 | yes |
17 | 2015 | 175878 | 504 | 0.359 | no |
18 | 2010 | 73439 | Troyes | 0.293 | no |
19 | 2014 | 157809 | Nations: The Dice Game | 0.281 | no |
20 | 2016 | 205158 | Codenames: Deep Undercover | 0.272 | no |
21 | 2016 | 198773 | Codenames: Pictures | 0.272 | no |
22 | 2014 | 155426 | Castles of Mad King Ludwig | 0.268 | no |
23 | 2007 | 31481 | Galaxy Trucker | 0.263 | no |
24 | 2014 | 157354 | Five Tribes | 0.262 | no |
25 | 2017 | 221805 | Breaking Bad: The Board Game | 0.239 | no |
26 | 2018 | 258036 | Between Two Castles of Mad King Ludwig | 0.237 | no |
27 | 2019 | 284818 | Caylus 1303 | 0.224 | no |
28 | 2016 | 204305 | Sherlock Holmes Consulting Detective: Jack the Ripper & West End Adventures | 0.224 | no |
29 | 2010 | 25292 | Merchants & Marauders | 0.213 | no |
30 | 2016 | 195856 | Bloodborne: The Card Game | 0.209 | no |
31 | 2019 | 276025 | Maracaibo | 0.207 | yes |
32 | 2017 | 197376 | Charterstone | 0.206 | no |
33 | 2013 | 146278 | Tash-Kalar: Arena of Legends | 0.201 | no |
34 | 2015 | 173346 | 7 Wonders Duel | 0.178 | yes |
35 | 2019 | 270971 | Era: Medieval Age | 0.177 | no |
36 | 2019 | 272453 | KeyForge: Age of Ascension | 0.170 | no |
37 | 2009 | 58798 | Cardcassonne | 0.167 | no |
38 | 2012 | 123096 | Space Cadets | 0.167 | no |
39 | 2014 | 164928 | Orléans | 0.165 | no |
40 | 2007 | 28720 | Brass: Lancashire | 0.165 | no |
41 | 2009 | 40692 | Small World | 0.164 | no |
42 | 2019 | 260710 | Amul | 0.162 | no |
43 | 2011 | 70919 | Takenoko | 0.153 | no |
44 | 2016 | 201808 | Clank!: A Deck-Building Adventure | 0.152 | no |
45 | 2019 | 283863 | The Magnificent | 0.146 | no |
46 | 2011 | 79828 | A Few Acres of Snow | 0.136 | no |
47 | 2015 | 181530 | Runebound (Third Edition) | 0.132 | no |
48 | 2018 | 247236 | Duelosaur Island | 0.129 | no |
49 | 2006 | 25613 | Through the Ages: A Story of Civilization | 0.129 | no |
50 | 2019 | 266936 | Slyville | 0.126 | no |
51 | 2013 | 147303 | Carcassonne: South Seas | 0.125 | no |
52 | 2010 | 68448 | 7 Wonders | 0.124 | yes |
53 | 2012 | 129904 | Shadows over Camelot: The Card Game | 0.123 | no |
54 | 2019 | 265285 | Queenz: To Bee or Not to Bee | 0.121 | no |
55 | 2012 | 122515 | Keyflower | 0.120 | no |
56 | 2008 | 38453 | Space Alert | 0.118 | no |
57 | 2011 | 98229 | Pictomania | 0.117 | no |
58 | 2000 | 478 | Citadels | 0.114 | no |
59 | 2016 | 198928 | Pandemic: Iberia | 0.114 | no |
60 | 2014 | 152241 | Ultimate Werewolf | 0.113 | no |
61 | 2008 | 38159 | Ultimate Werewolf: Ultimate Edition | 0.111 | no |
62 | 2017 | 174430 | Gloomhaven | 0.097 | no |
63 | 2011 | 92415 | Skull | 0.096 | no |
64 | 2016 | 193738 | Great Western Trail | 0.094 | yes |
65 | 2011 | 103343 | A Game of Thrones: The Board Game (Second Edition) | 0.093 | no |
66 | 2013 | 143693 | Glass Road | 0.092 | no |
67 | 2017 | 226320 | My Little Scythe | 0.092 | no |
68 | 2005 | 21829 | Sherwood | 0.090 | no |
69 | 2016 | 184919 | Greedy Greedy Goblins | 0.090 | no |
70 | 2018 | 244711 | Newton | 0.086 | no |
71 | 2009 | 55670 | Macao | 0.086 | no |
72 | 2018 | 199792 | Everdell | 0.083 | no |
73 | 1982 | 2511 | Sherlock Holmes Consulting Detective: The Thames Murders & Other Cases | 0.083 | no |
74 | 2017 | 234487 | Altiplano | 0.082 | no |
75 | 1999 | 88 | Torres | 0.078 | no |
76 | 2017 | 221194 | Dinosaur Island | 0.078 | no |
77 | 2014 | 159508 | AquaSphere | 0.076 | no |
78 | 2004 | 12495 | Fire & Axe: A Viking Saga | 0.075 | no |
79 | 1993 | 463 | Magic: The Gathering | 0.075 | no |
80 | 2017 | 232988 | The Castles of Burgundy: The Dice Game | 0.074 | no |
81 | 2009 | 45748 | Carcassonne: Wheel of Fortune | 0.074 | no |
82 | 2019 | 270673 | Silver & Gold | 0.074 | no |
83 | 2017 | 230751 | Carcassonne für 2 | 0.073 | no |
84 | 2014 | 148228 | Splendor | 0.072 | yes |
85 | 2004 | 9440 | Maharaja: The Game of Palace Building in India | 0.070 | no |
86 | 2016 | 156858 | Black Orchestra | 0.070 | no |
87 | 2007 | 31594 | In the Year of the Dragon | 0.070 | no |
88 | 2015 | 163166 | One Night Ultimate Werewolf: Daybreak | 0.067 | no |
89 | 1995 | 13 | Catan | 0.066 | no |
90 | 2017 | 232979 | Richard the Lionheart | 0.065 | no |
91 | 2011 | 77423 | The Lord of the Rings: The Card Game | 0.064 | no |
92 | 2012 | 120677 | Terra Mystica | 0.064 | no |
93 | 2018 | 256226 | Azul: Stained Glass of Sintra | 0.064 | no |
94 | 2012 | 121921 | Robinson Crusoe: Adventures on the Cursed Island | 0.063 | no |
95 | 2005 | 17053 | Sleeping Queens | 0.063 | no |
96 | 2017 | 204431 | One Night Ultimate Alien | 0.063 | no |
97 | 2013 | 124361 | Concordia | 0.063 | yes |
98 | 2015 | 180593 | The Bloody Inn | 0.062 | no |
99 | 2019 | 255293 | One Night Ultimate Super Villains | 0.062 | no |
100 | 2008 | 37380 | Roll Through the Ages: The Bronze Age | 0.062 | no |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.9 |
Decision Tree | roc_auc | binary | 0.7 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think yourwhiteshadow is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2019 | 286096 | Tapestry | 0.861 | no |
2011 | 96848 | Mage Knight Board Game | 0.685 | no |
2018 | 205896 | Rising Sun | 0.613 | no |
2019 | 285774 | Marvel Champions: The Card Game | 0.543 | no |
2013 | 134453 | The Little Prince: Make Me a Planet | 0.464 | no |
What games does the model think yourwhiteshadow is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2019 | 284083 | The Crew: The Quest for Planet Nine | 0 | yes |
2017 | 230303 | Unlock!: Mystery Adventures – The House on the Hill | 0 | yes |
2018 | 244522 | That's Pretty Clever! | 0 | yes |
2018 | 244992 | The Mind | 0 | yes |
2019 | 271869 | Sushi Roll | 0 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Android: Netrunner | The Little Prince: Make Me a Planet | Nations: The Dice Game | 504 | Arkham Horror: The Card Game | Breaking Bad: The Board Game | Rising Sun | Tapestry |
2 | Space Cadets | Tash-Kalar: Arena of Legends | Castles of Mad King Ludwig | 7 Wonders Duel | Scythe | Charterstone | Concordia Venus | Marvel Champions: The Card Game |
3 | Shadows over Camelot: The Card Game | Carcassonne: South Seas | Five Tribes | Runebound (Third Edition) | Citadels | Gloomhaven | Between Two Castles of Mad King Ludwig | Aftermath |
4 | Keyflower | Glass Road | Orléans | One Night Ultimate Werewolf: Daybreak | Terraforming Mars | My Little Scythe | Duelosaur Island | Clank!: Legacy – Acquisitions Incorporated |
5 | Terra Mystica | Concordia | Ultimate Werewolf | The Bloody Inn | Codenames: Pictures | Altiplano | Newton | Last Bastion |
6 | Robinson Crusoe: Adventures on the Cursed Island | The Builders: Middle Ages | AquaSphere | One Night Revolution | Codenames: Deep Undercover | Dinosaur Island | Everdell | Caylus 1303 |
7 | Star Wars: The Card Game | A Study in Emerald | Splendor | The Little Prince: Rising to the Stars | Sherlock Holmes Consulting Detective: Jack the Ripper & West End Adventures | The Castles of Burgundy: The Dice Game | Azul: Stained Glass of Sintra | Maracaibo |
8 | Star Wars: X-Wing Miniatures Game | Impulse | Abyss | A Game of Thrones: The Card Game (Second Edition) | Bloodborne: The Card Game | Carcassonne für 2 | The World of SMOG: Rise of Moloch | Era: Medieval Age |
9 | Merchant of Venus (Second Edition) | Rory's Story Cubes: Prehistoria | Three Kingdoms Redux | Elysium | Clank!: A Deck-Building Adventure | Richard the Lionheart | Pandemic: Fall of Rome | KeyForge: Age of Ascension |
10 | Suburbia | Euphoria: Build a Better Dystopia | Deception: Murder in Hong Kong | One Night Ultimate Vampire | Pandemic: Iberia | One Night Ultimate Alien | Star Wars: X-Wing (Second Edition) | Amul |
11 | Noah | Rococo | La Granja | The Voyages of Marco Polo | Great Western Trail | SpyNet | KeyForge: Call of the Archons | The Magnificent |
12 | Open Sesame | City of Iron | Mythotopia | Viticulture Essential Edition | Greedy Greedy Goblins | Century: Spice Road | Château Aventure | Slyville |
13 | Carcassonne: Winter Edition | Sanssouci | Lost Valley: The Yukon Goldrush 1896 | The King Is Dead | Black Orchestra | Sherlock Holmes Consulting Detective: Carlton House & Queen's Park | Nyctophobia | Queenz: To Bee or Not to Bee |
14 | Yedo | Room 25 | Sheriff of Nottingham | The Game | Junta: Las Cartas | Calimala | Book of Dragons | Silver & Gold |
15 | Game of Thrones: The Card Game | Colonialism | King of New York | Stronghold: 2nd edition | Dominion (Second Edition) | Sagrada | Carpe Diem | One Night Ultimate Super Villains |
16 | Pax Porfiriana | Ladies & Gentlemen | Warhammer 40,000: Conquest | Between Two Cities | Honshū | Bali | Coin & Crown | Jeff Davis: The Confederacy at War |
17 | Smash Up | Kniffel: Das Kartenspiel | Arcadia Quest | Cat Tower | Star Trek: Frontiers | Unlock!: Escape Adventures – Doo-Arann Dungeon | Arraial | Crusader Kings |
18 | Love Letter | Crossing | Spurs: A Tale in the Old West | Marvel Dice Masters: Age of Ultron | Aeon's End | Twilight Imperium: Fourth Edition | Unlock!: Escape Adventures – In Pursuit of Cabrakan | A Rusty Throne |
19 | Rattus Cartus | Time 'n' Space | Black Fleet | Het Koninkrijk Dominion | Smash Up: Cease and Desist | Queendomino | AuZtralia | Mental Blocks |
20 | The Last Banquet | Terror in Meeple City | Linko! | Stowaway 52 | Conan | Lost in Time | Brass: Birmingham | TIME Stories Revolution: Damien 1958 NT |
21 | Divinare | Viticulture | Viceroy | Coffee Roaster | Star Wars: Rebellion | Spirit Island | Architects of the West Kingdom | Blitzkrieg!: World War Two in 20 Minutes |
22 | Seasons | World of Tanks: Rush | Akrotiri | Dragon Tides | 13 Days: The Cuban Missile Crisis | Pandemic: Rising Tide | Neon Gods | Black Angel |
23 | Kairo | Two Rooms and a Boom | Gaïa | Mombasa | Game of Thrones: The Iron Throne | Legend of the Five Rings: The Card Game | Pictomania (Second Edition) | Machi Koro Legacy |
24 | Okiya | Nations | Colt Express | Tesla vs. Edison: War of Currents | The Oracle of Delphi | Whitehall Mystery | Unlock!: Heroic Adventures | The Mind Extreme |
25 | Wiz-War (Eighth Edition) | Amerigo | Desperados of Dice Town | Salem 1692 | Pandemic: Reign of Cthulhu | Smash Up: What Were We Thinking? | The Villagers | Villagers |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
yourwhiteshadow | owned | validation | GLM | roc_auc | 0.881 |
yourwhiteshadow | owned | validation | Decision Tree | roc_auc | 0.668 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 316377 | 7 Wonders (Second Edition) | 0.392 | no |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.114 | no |
2020 | 298572 | Cosmic Encounter Duel | 0.104 | no |
2020 | 316412 | The LOOP | 0.088 | no |
2020 | 318084 | Furnace | 0.085 | no |
2020 | 292333 | Cowboys II: Cowboys & Indians Edition | 0.073 | no |
2020 | 293296 | Splendor: Marvel | 0.072 | no |
2020 | 298371 | Wild Space | 0.066 | no |
2020 | 312804 | Pendulum | 0.049 | no |
2020 | 296626 | Sonora | 0.044 | no |
2020 | 282954 | Paris | 0.042 | no |
2020 | 256940 | Krosmaster: Blast | 0.041 | no |
2020 | 245224 | La Belle Époque | 0.039 | no |
2020 | 256317 | Guild Master | 0.039 | no |
2020 | 308765 | Praga Caput Regni | 0.034 | no |
2020 | 302425 | Unlock!: Mythic Adventures | 0.033 | no |
2020 | 319966 | The King Is Dead: Second Edition | 0.031 | no |
2020 | 303672 | Trek 12: Himalaya | 0.030 | no |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.029 | no |
2020 | 300010 | Dragomino | 0.028 | no |
2020 | 298638 | Sheriff of Nottingham: 2nd Edition | 0.028 | no |
2020 | 304420 | Bonfire | 0.028 | no |
2020 | 306040 | Merv: The Heart of the Silk Road | 0.026 | no |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.026 | no |
2020 | 301607 | KeyForge: Mass Mutation | 0.026 | no |
2020 | 299179 | Chancellorsville 1863 | 0.025 | no |
2020 | 297030 | Tekhenu: Obelisk of the Sun | 0.025 | no |
2020 | 271524 | TIME Stories Revolution: A Midsummer Night | 0.025 | no |
2020 | 262208 | Dungeon Drop | 0.025 | no |
2020 | 312267 | Star Wars: Unlock! | 0.024 | no |
2020 | 311193 | Anno 1800 | 0.023 | no |
2020 | 301919 | Pandemic: Hot Zone – North America | 0.023 | no |
2020 | 296151 | Viscounts of the West Kingdom | 0.021 | no |
2020 | 308416 | Tapeworm | 0.021 | no |
2020 | 302310 | Nanaki | 0.021 | no |
2020 | 327913 | Unlock!: Timeless Adventures – Arsène Lupin und der große weiße Diamant | 0.020 | no |
2020 | 302417 | Mia London and the Case of the 625 Scoundrels | 0.020 | no |
2020 | 296345 | Sherlock Holmes Consulting Detective: The Baker Street Irregulars | 0.019 | no |
2020 | 287742 | TIME Stories Revolution: The Hadal Project | 0.018 | no |
2020 | 295905 | Cosmic Frog | 0.018 | no |
2020 | 308652 | Age of Dogfights: WW1 | 0.018 | no |
2020 | 311927 | Long Live the King: A Game of Secrecy and Subterfuge | 0.016 | no |
2020 | 301716 | Glasgow | 0.016 | no |
2020 | 318983 | Faiyum | 0.013 | no |
2020 | 299592 | Beez | 0.013 | no |
2020 | 293579 | Totemic | 0.012 | no |
2020 | 281655 | High Frontier 4 All | 0.012 | no |
2020 | 291874 | Dwergar | 0.012 | no |
2020 | 302260 | Abandon All Artichokes | 0.011 | no |
2020 | 272739 | Clinic: Deluxe Edition | 0.011 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Published | ID | Name | Pr(Owned) | Owned |
2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.472 | no |
2022 | 331106 | The Witcher: Old World | 0.335 | no |
2021 | 332944 | Sobek: 2 Players | 0.226 | no |
2021 | 285967 | Ankh: Gods of Egypt | 0.185 | no |
2023 | 347909 | Rogue Angels: Legacy of the Burning Suns | 0.177 | no |
2021 | 339789 | Welcome to the Moon | 0.163 | no |
2021 | 339906 | The Hunger | 0.154 | no |
2021 | 298102 | Roll Camera!: The Filmmaking Board Game | 0.113 | no |
2022 | 326945 | Castles of Mad King Ludwig: Collector's Edition | 0.093 | no |
2021 | 331635 | Kameloot | 0.082 | no |
2021 | 291572 | Oath: Chronicles of Empire and Exile | 0.082 | yes |
2021 | 273330 | Bloodborne: The Board Game | 0.065 | no |
2022 | 310873 | Carnegie | 0.057 | no |
2021 | 319793 | Happy City | 0.056 | no |
2021 | 341169 | Great Western Trail (Second Edition) | 0.054 | no |
2021 | 338980 | Eastern Empires | 0.051 | no |
2021 | 291859 | Riftforce | 0.051 | no |
2021 | 303954 | Pax Viking | 0.046 | no |
2021 | 343905 | Boonlake | 0.045 | no |
2022 | 317511 | Tindaya | 0.043 | no |
2021 | 290236 | Canvas | 0.043 | no |
2021 | 337787 | Summer Camp | 0.042 | no |
2022 | 334065 | Verdant | 0.040 | no |
2021 | 342942 | Ark Nova | 0.040 | no |
2021 | 316287 | Quest | 0.036 | no |
2021 | 295947 | Cascadia | 0.036 | yes |
2021 | 283387 | Rocketmen | 0.034 | no |
2021 | 328479 | Living Forest | 0.033 | no |
2021 | 329465 | Red Rising | 0.033 | no |
2021 | 340041 | Kingdomino Origins | 0.031 | no |
2021 | 339484 | Savannah Park | 0.031 | no |
2021 | 281248 | Cape May | 0.029 | no |
2021 | 348461 | Castle Break | 0.028 | no |
2021 | 313730 | Harsh Shadows | 0.028 | no |
2021 | 311920 | Ultimate Werewolf: Extreme | 0.027 | no |
2021 | 249277 | Brazil: Imperial | 0.027 | no |
2021 | 308989 | Bristol 1350 | 0.026 | no |
2021 | 304985 | Dark Ages: Holy Roman Empire | 0.025 | no |
2021 | 295535 | Dark Ages: Heritage of Charlemagne | 0.025 | no |
2021 | 256680 | Return to Dark Tower | 0.024 | no |
2021 | 338834 | MicroMacro: Crime City – Full House | 0.024 | no |
2021 | 298069 | Cubitos | 0.024 | no |
2021 | 316080 | KeyForge: Dark Tidings | 0.023 | no |
2021 | 344258 | That Time You Killed Me | 0.023 | no |
2021 | 299255 | Vienna Connection | 0.022 | no |
2021 | 324242 | Sheepy Time | 0.021 | no |
2021 | 282776 | Tumble Town | 0.021 | no |
2022 | 349793 | Age of Rome | 0.019 | no |
2021 | 314491 | Meadow | 0.019 | no |
2021 | 318184 | Imperium: Classics | 0.019 | no |
2021 | 343847 | Dustbiters | 0.017 | no |
2021 | 329670 | Pandemic: Hot Zone – Europe | 0.017 | no |
2021 | 340237 | Wonder Book | 0.016 | no |
2021 | 305682 | Rolling Realms | 0.016 | no |
2021 | 345976 | System Gateway (fan expansion for Android: Netrunner) | 0.016 | no |
2021 | 307862 | Dollars to Donuts | 0.015 | no |
2021 | 313841 | Lunar Base | 0.015 | no |
2021 | 304324 | Dive | 0.015 | no |
2021 | 306202 | Philosophia: Floating World | 0.015 | no |
2021 | 336195 | League of Dungeoneers | 0.015 | no |
2022 | 346199 | A Game of Thrones: B'Twixt | 0.015 | no |
2021 | 343562 | Horrified: American Monsters | 0.015 | no |
2021 | 340909 | Gloomholdin' | 0.015 | no |
2021 | 257706 | Zoo-ography | 0.014 | no |
2021 | 340455 | King of the Valley | 0.014 | no |
2021 | 331685 | Hit the Silk! | 0.014 | no |
2022 | 254127 | Europa Universalis: The Price of Power | 0.014 | no |
2022 | 230967 | Verrix | 0.013 | no |
2021 | 314088 | Agropolis | 0.013 | no |
2023 | 298086 | The Fog: Escape from Paradise | 0.013 | no |
2021 | 295931 | Granada: Last Stand of the Moors – 1482-1492 | 0.013 | no |
2021 | 294986 | Necromolds: Monster Battles | 0.013 | no |
2022 | 332393 | Bridge City Poker | 0.012 | no |
2022 | 342900 | Earthborne Rangers | 0.012 | no |
2021 | 336794 | Galaxy Trucker | 0.012 | no |
2021 | 307703 | Dawn of Battle | 0.012 | no |
2021 | 340466 | Unfathomable | 0.012 | yes |
2022 | 345584 | Mindbug | 0.012 | no |
2022 | 304051 | Creature Comforts | 0.012 | no |
2021 | 318996 | Welcome to Sysifus Corp | 0.012 | no |
2022 | 330950 | Age of Galaxy | 0.012 | no |
2022 | 283137 | Human Punishment: The Beginning | 0.012 | no |
2021 | 341530 | Super Mega Lucky Box | 0.012 | no |
2021 | 271529 | Botanik | 0.011 | no |
2021 | 346703 | 7 Wonders: Architects | 0.011 | no |
2022 | 295770 | Frosthaven | 0.011 | no |
2021 | 319792 | Fly-A-Way | 0.011 | no |
2021 | 322588 | Origins: First Builders | 0.011 | no |
2022 | 333255 | Keep the Heroes Out! | 0.011 | no |
2021 | 339790 | Cocktail | 0.011 | no |
2022 | 342444 | Black Rose Wars: Rebirth | 0.011 | no |
2021 | 289550 | Lions of Lydia | 0.011 | no |
2021 | 344768 | Mobile Markets: A Smartphone Inc. Game | 0.011 | no |
2022 | 258779 | Planet Unknown | 0.010 | no |
2022 | 251661 | Oathsworn: Into the Deepwood | 0.010 | no |
2021 | 280984 | Ruins: Death Binder | 0.010 | no |
2021 | 320136 | Naruto: Ninja Arena | 0.010 | no |
2021 | 310192 | Overboss: A Boss Monster Adventure | 0.010 | no |
2022 | 305096 | Endless Winter: Paleoamericans | 0.010 | no |
2021 | 260524 | Beyond Humanity: Colonies | 0.010 | no |